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nanoim-microturn-tiny
jlov7/nanoim-microturn-tiny
nanoim-microturn-tiny is a machine learning model from jlov7. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as mit.
This model repository package contains tiny from-scratch PyTorch checkpoints for the nanoIM symbolic temporal-aliasing lab.
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Public
Updated Jun 1, 2026
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2 MB
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.pt2 MB · 90%
At a glance
- Library
- pytorch
- License
- mit
- Access
- Public
- Created
- Jun 1, 2026
- Updated
- Jun 1, 2026
- SHA
- 91de7a60
Datasets
Model card
From the Hugging Face model README
nanoIM MicroTurn Tiny Models
This model repository package contains tiny from-scratch PyTorch checkpoints for the nanoIM symbolic temporal-aliasing lab.
What Is Included
| Path | Description |
|---|---|
checkpoints/tiny/best.pt | Mini-suite GRU checkpoint. |
checkpoints/hard/best.pt | Hard-suite GRU checkpoint. |
checkpoints/noisy/best.pt | Noisy-suite GRU checkpoint. |
checkpoints/transformer_hard/best.pt | Hard-suite tiny Transformer checkpoint. |
configs/*.yaml | Training configs used to produce the checkpoints. |
reports/*.json | Scorecards, sweeps, controls, and release verification evidence. |
Parameter Counts
| Checkpoint | Parameters |
|---|---|
checkpoints/tiny/best.pt | 19,288 |
checkpoints/hard/best.pt | 65,604 |
checkpoints/noisy/best.pt | 115,256 |
checkpoints/transformer_hard/best.pt | 257,988 |
Largest model is ~258K parameters. All others are under 120K.
Intended Use
Use these checkpoints to reproduce nanoIM scorecards and inspect how a native micro-turn representation separates alias pairs that a transcript-only baseline cannot separate.
The models consume symbolic micro-turn features. They are not text generators, not chat models, and not production assistants.
Loading
The nanoIM evaluator loads checkpoints with torch.load(..., weights_only=True).
From the source repository, evaluate the committed source-tree checkpoint:
uv run python -m nanoim.eval --checkpoint runs/noisy/full/seed_7/best.pt --suite noisy --data data/noisy.jsonl --out reports/noisy_scorecard.json
From this generated Hugging Face model repository, install or clone the nanoIM source package, then point the evaluator at the model-repo checkpoint path:
uv run python -m nanoim.eval --checkpoint checkpoints/noisy/best.pt --suite noisy --data ../dataset/data/noisy.jsonl --out reports/noisy_scorecard.json
The generated model repo also includes MANIFEST.json and SHA256SUMS so a reviewer can bind checkpoint files to the release manifest before evaluation.
Evaluation Summary
| Model | Suite | TAA | Delta vs transcript oracle |
|---|---|---|---|
| MicroTurn Tiny GRU | hard | 1.00 | +0.50 |
| MicroTurn Tiny GRU | noisy | 1.00 | +0.50 |
| MicroTurn Tiny Transformer | hard | 1.00 | +0.50 |
The rule harness and a memorized field-lookup table also reach 1.00. The result is about the transcript representation's 0.50 ceiling, not about model superiority.
Transcript-only paired separation remains 0.00 on the hard and noisy suites.
Limitations
The checkpoints prove a controlled symbolic representation result. They do not perform ASR, TTS, visual perception, natural language generation, tool execution, or realtime dialogue management.
- Library
- pytorch
- License
- mit
- Created
- Jun 1, 2026
- Updated
- Jun 1, 2026